Quest Commercial Scale CCS – The First Year
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Quest is a commercial scale, fully integrated carbon capture and storage project and the first connected to the oil sands. Located in Alberta, Canada, Quest is designed to capture and safely store more than one million tonnes of CO2 each year – equal to the emissions from about 250,000 cars. This represents one-third of the emissions from the Scotford Upgrader, which transforms bitumen into crude for refining into fuel and other products. CO2 injection began on the 23rd of August 2015 and the project has successfully captured and stored over 1 million tonnes of CO2 in its first year of operation. Startup was successful and followed a staged approach. First, the three capture units were commissioned with a period of run-in, then the compression and conditioning, followed by displacement of the nitrogen from the pipeline, before finally moving to injecting around 3000 tonnes per day into the Basal Cambrian Sandstone saline aquifer; using two of three available wells. The injectivity and pressure dissipation has been exceptionally good allowing the third well to be reserved for interference testing. During the start of injection, the production technology team worked in the Scotford control room and ran real time transient flow simulations of the CO2 expansion across the well head chokes and into the wells. This allowed the operators to pro-actively manage the ramp up. To our knowledge this is the first time that this has been done. The onshore storage is deploying cutting edge monitoring technology – fibre optic vertical seismic profiles and line of sight surface CO2 detection; along with microsesimic and extensive pressure monitoring. This paper outlines the experience with starting up the facilities and wells and will present the results of the first year of operation. It will present the experience from stakeholder engagement, the capture plant, the compression system, pipeline, wells and monitoring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it